基于围猎改进哈里斯鹰优化的粒子滤波方法

李冀; 周战洪; 贺红林; 刘文光; 李怡庆

doi:10.11999/JEIT220532

基于围猎改进哈里斯鹰优化的粒子滤波方法

doi: 10.11999/JEIT220532

南昌航空大学航空制造工程学院南昌 330063

基金项目: 国家自然科学基金(51665040)，江西省自然科学基金重点项目(20202ACB202003)，江西省自然科学基金(20212BAB211015)

详细信息

作者简介:
李冀：男，讲师，博士，主要研究方向为群智能优化算法、系统优化等

周战洪：男，硕士生，研究方向为粒子滤波方法、多传感器数据融合

贺红林：男，教授，博士，主要研究方向为精密驱动系统设计与优化

刘文光：男，教授，博士，主要研究方向为机器人技术、目标跟踪

李怡庆：男，讲师，博士，主要研究方向为粒子滤波方法、航空机电设备健康管理

通讯作者:
李冀　lj@nchu.edu.cn

中图分类号: TN911.7; TN713
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出版历程
- 收稿日期: 2022-04-27
- 修回日期: 2022-07-25
- 录用日期: 2022-08-02
- 网络出版日期: 2022-08-04
- 刊出日期: 2023-06-10

A Particle Filter Method Based on Harris Hawks Optimization Improved by Encircling Strategy

School of Aeronautical Manufacturing, Nanchang Hangkong University, Nanchang 330063, China

Funds: The National Natural Science Foundation of China (51665040), The Key Projects of Natural Science Foundation of Jiangxi Province (20202ACB202003), The Natural Science Foundation of Jiangxi Province (20212BAB211015)

摘要

摘要: 针对标准粒子滤波过程的权值退化和样本贫化问题，该文结合融入围猎策略的哈里斯鹰优化算法设计一种群智能优化粒子滤波方法(EHHOPF)。首先，引入围猎策略替代哈里斯鹰优化算法全局搜索策略以适配粒子滤波环境；其次，采用Sigmoid函数构建非线性猎物逃逸能量平衡算法的探索阶段和开发阶段；最后构建选择比例因子融合开发阶段捕猎策略并采用非线性猎物跳跃强度保证算法收敛效率。仿真结果表明，与标准粒子滤波以及磷虾算法、蝙蝠算法、布谷鸟算法、灰狼算法优化的粒子滤波方法相比，基于围猎改进哈里斯鹰优化的粒子滤波方法有效提升了系统状态估计精度、滤波稳定性和滤波实时性。
- 粒子滤波 /
- 哈里斯鹰优化算法 /
- 权值退化 /
- 样本贫化
Abstract: To deal with the weight degradation and sample impoverishment problems of particle filter, a Particle Filter based on Harris Hawks Optimization improved by Encircling strategy (EHHOPF) is designed. Firstly, the global search strategy in Harris Hawks Optimization is replaced by an encircling prey strategy to fit the filtering environment. Additionally, Sigmoid function is introduced to construct the nonlinear prey escaping energy to achieve the balance between exploration and exploitation. Lastly, the selection scale factor is proposed to simplify the selection mechanism of searching strategies and nonlinear dynamic prey jump strength is constructed to guarantee the convergence efficiency as well. The simulation results exhibited that the proposed particle filter can effectively improve the state estimation accuracy, filtering stability and real-time performance than the standard particle filter and particle filters optimized by krill herd algorithm, bat algorithm, cuckoo search algorithm and grey wolf optimizer.
- Particle filter /
- Harris hawks optimization /
- Weight degradation /
- Sample impoverishment

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图 1 猎物逃逸能量变化对比

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图 2 滤波状态估计 (N=20)

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图 3 估计误差绝对值 (N=20)

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图 4 滤波状态估计 (N=100)

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图 5 估计误差绝对值 (N=100)

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图 6 不同时刻粒子分布

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图 7 不同方法的滤波实时性

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图 8 不同粒子数下各滤波方法均方根误差

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图 9 目标跟踪轨迹

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图 10 目标距离偏差变化

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表 1 群智能优化滤波方法参数设置

滤波方法	${w_1}$	${w_2}$	$ {N^{\max }} $	$ {V_f} $	$ {D^{\max }} $	$\alpha $	$\gamma $	${f_{\min }}$	${f_{\max }}$	${p_a}$
IKHPF	0.2	0.6	0.08	1.2	0.01	–	–	–	–	–
BAPF	–	–	–	–	–	0.5	0.5	0	2	–
ICSPF	–	–	–	–	–	–	–	–	–	0.75

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表 2 不同粒子滤波算法仿真结果比较

滤波方法	RMSE_mean			RMSE_var			T_mean(s)
滤波方法	20	50	100	20	50	100	20	50	100
PF	0.7863	0.6188	0.5231	0.0373	0.0419	0.0261	2.71E-03	6.23E-03	0.0113
IKHPF	0.0691	0.0371	0.0357	2.36E-03	1.17E-03	9.54E-04	0.0214	0.0582	0.1319
BAPF	0.0430	0.0313	0.0286	1.24E-04	3.19E-05	8.38E-06	0.0134	0.0329	0.0633
ICSPF	0.0225	0.0138	0.0112	1.82E-04	3.04E-05	9.86E-06	9.79E-03	0.0181	0.0310
GWOPF	0.0508	0.0295	0.0264	5.57E-03	1.22E-05	7.05E-06	3.06E-03	7.33E-03	0.0141
EHHOPF	0.0193	0.0116	9.02E-03	7.11E-05	6.91E-06	3.45E-06	6.04E-03	0.0148	0.0271

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表 3 不同滤波算法目标跟踪结果(m)

滤波方法	RMSE_mean				RMSE_var				SGAD		MAD
滤波方法	P_x	V_x	P_y	V_y	P_x	V_x	P_y	V_y	均值	方差	均值	方差
PF	10.0260	0.6644	11.8082	0.8561	17.0158	0.0621	24.5250	0.1248	58.4830	1036.36	1.9494	1.1515
IKHPF	4.9863	0.6188	5.2403	0.5968	11.4143	0.1528	12.4253	0.0804	35.6843	600.255	1.1895	0.6669
BAPF	14.8241	1.2136	17.3307	1.4562	39.7658	0.1654	51.4222	0.3707	72.6756	1718.71	2.4225	1.9097
ICSPF	40.4398	0.1106	63.5023	0.2041	277.389	4.36E-04	1301.48	5.41E-04	261.777	13191.3	8.7259	14.657
GWOPF	4.5594	0.1912	5.0885	0.2513	4.4170	0.0119	6.0523	0.0238	28.4354	196.586	0.9478	0.2184
EHHOPF	4.4762	0.2249	4.9962	0.2871	3.0997	8.00E-03	4.2422	0.0148	27.9863	144.975	0.9329	0.1611

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